TimeMachine

Abstract

We present a method called TIMEMACHINE to generate a timeline of events and relations for entities in a knowledge base. For example for an actor, such a timeline should show the most important professional and personal milestones and relationships such as works, awards, collaborations, and family relationships. We develop three orthogonal timeline quality criteria that an ideal timeline should satisfy: (1) it shows events that are relevant to the entity; (2) it shows events that are temporally diverse, so they distribute along the time axis, avoiding visual crowding and allowing for easy user interaction, such as zooming in and out; and (3) it shows events that are content diverse, so they contain many different types of events (e.g., for an actor, it should show movies and marriages and awards, not just movies). We present an algorithm to generate such timelines for a given time period and screen size, based on submodular optimization and web-co-occurrence statistics with provable performance guarantees. A series of user studies using Mechanical Turk shows that all three quality criteria are crucial to produce quality timelines and that our algorithm significantly outperforms various baseline and state-of-the-art methods.

EventKG – the hub of event knowledge on the web – and biographical timeline generation

“…Therefore, we attempt to learn to discard the temporal relations involving events that happened too early for the entity timeline. This had been also observed by Althoff et al [2] who implemented a rule to discard such relations. In addition to that, our temporal features could help to learn whether some events may be more relevant at specific stages of the entity's life or existence.…”

“…Timeline generation is an active research area [2,3], where the focus is to generate a timeline (i.e. a chronologically ordered selection) of events and temporal relations for entities from a knowledge graph.…”

“…Efficiently accessing and analysing large-scale eventcentric and temporal information is crucial for a variety of real-world applications in the fields of Semantic Web, NLP and Digital Humanities. In Semantic Web and NLP, these applications include timeline generation [2,3] and Question Answering [4,5]. In Digital Humanities, multilingual event repositories can facilitate cross-cultural studies analysing languagespecific and community-specific views on historical and contemporary events (examples of such studies can be seen in [6,7]).…”

“…Timelines are an effective method to provide a visual overview of entity-centric temporal information, such as temporal relations in a knowledge graph [2]. In particular, biographical timelines describe significant happenings in a person's life and typically include events of major relevance from the personal perspec- tive such as birth, education and career.…”

“…Overall, our contributions in this article are as follows: 1 We formally define the concept of a temporal knowledge graph T KG that incorporates entities, events and temporal relations. 2 We present an instantiation of T KG: EventKG -a multilingual RDF knowledge graph that incorporates over 690 thousand events and over 2.3 million temporal relations in V1.1. We provide insights on the extraction and fusion methods adopted to generate the EventKG knowledge graph and their quality.…”